Method to correct magnetic field/phase variations in proton resonance frequency shift thermometry in magnetic resonance imaging

ABSTRACT

PRF shift MRI data is acquired. The PRF shift MRI data may include signals affected by both a desired PRF shift and an undesired PRF shift. Thus, example systems and methods describe manipulating the PRF shift MRI data to make it substantially free of the effects of the undesired PRF shift, which facilitates displaying certain MRI images based on the desired PRF shift.  
     It is emphasized that this abstract is provided to comply with the rules requiring an abstract that will allow a searcher or other reader to quickly ascertain the subject matter of the application. It is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims.

CROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. ProvisionalApplication No. 60/380,720 titled “PRF Shift Thermometry in MRI System”,filed May 15, 2002, which is incorporated herein by reference.

TECHNICAL FIELD

[0002] This application relates to the magnetic resonance imaging (MRI)arts. It finds particular application to improving the fidelity ofimages based on proton resonance frequency (PRF) shifting that areaffected by both desired and undesired phase shifting events. Although amagnetic resonance imaging system is described herein, the method may beapplicable to MRI, NMR and/or other applications that experience phasevariations.

BACKGROUND

[0003] MRI systems acquire diagnostic images without relying on ionizingradiation. Instead, MRI employs strong, static magnetic fields,radio-frequency (RF) pulses of energy, and time varying magnetic fieldgradient waveforms. Unfortunately, the strong, static magnetic fieldsmay sometimes experience temporal, spatial, field strength, and/or othervariations, which may impact imaging applications that rely on protonresonant frequency shifting and/or other applications (e.g., velocitymeasurement) using phase shifting.

[0004] MRI is a non-invasive procedure that employs nuclearmagnetization and radio waves to produce internal pictures of a subject.Two or three-dimensional diagnostic image data is acquired forrespective “slices” of a subject area. These data slices typicallyprovide structural detail having, for example, a resolution of onemillimeter or better. Programmed steps for collecting data, which isused to generate the slices of the diagnostic image, are known as an MRimage pulse sequence. The MR image pulse sequence includes generatingmagnetic field gradient waveforms applied along up to three axes, andone or more RF pulses of energy. The set of gradient waveforms and RFpulses are repeated a number of times to collect sufficient data toreconstruct the image slices.

[0005] Data is acquired during successive repetitions of an MR imagingpulse sequence or excitation. Ideally, there is little or no variationin the nuclear magnetization and the spatio-temporal characteristics ofthe background magnetic field during the respective excitations.However, variations can occur. When variations occur, data used tocreate an image between respective excitations may have peak signallocations that become misaligned. Thus, the nuclear magnetizationvariations may degrade the quality of the MR data used to produce theimages, particularly in PRF shift applications.

[0006] Sources of background phase variation can dominate the featuresof phase images used to generate temperature difference maps in PRF MRthermometry. This is particularly problematic at low magnetic fieldstrengths (e.g., 0.2T resistive magnets). These errors exist, albeit toa lesser extent, when performed on higher field and/or superconductingsystems.

SUMMARY

[0007] The following presents a simplified summary of methods, systems,application programming interfaces (API), and computer readable mediaemployed with PRF shift imaging (e.g., thermometry) in an MRI system, tofacilitate providing a basic understanding of these items. This summaryis not an extensive overview and is not intended to identify key orcritical elements of the methods, systems, computer readable media, andso on or to delineate the scope of these items. This summary provides aconceptual introduction in a simplified form as a prelude to the moredetailed description that is presented later.

[0008] An example system acquires a reference MRI data, then acquiressubsequent MRI data to compare to the reference MRI data. For example,temperature variations can be related to proton resonant frequencyvariation. This allows temperature changes to be measured with MRIthrough signal frequency variation and thus phase variation over time.The example system analyzes and then manipulates input data that may beaffected by undesired phase shifting events (e.g., magnetic fieldvariation in space and/or time) to facilitate mitigating the effects ofthe undesired phase shifting events. The example system then analyzesand manipulates the processed input data to study (e.g. identify,quantify) phase shifts related to desired phase shifting events (e.g.,heating a portion of an object to be imaged).

[0009] In one example, some or all of the components of the examplesystems and methods may be implemented as software executable by one ormore computers or other processing devices. They may be embodied in acomputer readable medium like a magnetic disk, digital compact disk,electronic memory, persistent and/or temporary memories, and so on asknown in the art. They may also be embodied as hardware or a combinationof hardware and software.

[0010] Certain illustrative example methods, systems, APIs, and computerreadable media are described herein in connection with the followingdescription and the annexed drawings. These examples are indicative,however, of but a few of the various ways in which the principles of themethods, systems, APIs, and computer readable media may be employed andthus are intended to be inclusive of equivalents. Other advantages andnovel features may become apparent from the following detaileddescription when considered in conjunction with the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

[0011]FIG. 1 illustrates an example MRI system.

[0012]FIG. 2 illustrates an example system for mitigating the effects ofundesired phase shifting events.

[0013]FIG. 3 illustrates an example system for PRF shift thermometry.

[0014]FIG. 4 illustrates an example region of interest in a field ofview.

[0015]FIG. 5 illustrates an example plot of sampled data points employedin indirect interpolation.

[0016]FIG. 6 is a flow chart of an example method for mitigating theeffects of undesired phase shifting events.

[0017]FIG. 7 is a flow chart of an example method for PRF shiftthermometry.

[0018]FIG. 8 illustrates an example MRI system.

[0019]FIG. 9 illustrates an example computing environment with whichcomputer executable systems and methods can interact.

[0020]FIG. 10 illustrates an example API associated with PRF shiftimaging.

[0021]FIG. 11 illustrates example indirect interpolation.

DETAILED DESCRIPTION

[0022] Example methods, systems, APIs, and computer media are nowdescribed with reference to the drawings where like reference numeralsare used to refer to like elements throughout. In the followingdescription, for purposes of explanation, numerous specific details areset forth to explain the methods, systems, APIs, and computer readablemedia. It may be evident, however, that the methods, systems, APIs, andcomputer readable media can be practiced without these specific details.In other instances, well-known structures and devices are shown in blockdiagram form in order to simplify description.

[0023] As used in this application, the term “computer component” refersto a computer-related entity, either hardware, firmware, software, acombination thereof, or software in execution. For example, a computercomponent can be, but is not limited to being, a process running on aprocessor, a processor, an object, an executable, a thread of execution,a program, and a computer. By way of illustration, both an applicationrunning on a server and the server can be computer components. One ormore computer components can reside within a process and/or thread ofexecution and a computer component can be localized on one computerand/or distributed between two or more computers.

[0024] “Software”, as used herein, includes but is not limited to one ormore computer readable and/or executable instructions that cause acomputer or other electronic device to perform functions, actions,and/or behave in a desired manner. The instructions may be embodied invarious forms like routines, algorithms, modules or programs includingseparate applications or code from dynamically linked libraries.Software may also be implemented in various forms like a stand-aloneprogram, a function call, a servelet, an applet, instructions stored ina memory, part of an operating system or other type of executableinstructions. It will be appreciated by one of ordinary skill in the artthat the form of software is dependent on, for example, requirements ofa desired application, the environment in which it runs, and/or thedesires of a designer/programmer or the like.

[0025] “Logic”, as used herein, includes but is not limited to hardware,firmware, software and/or combinations of each to perform a function(s)or an action(s). For example, based on a desired application or needs,logic may include a software controlled microprocessor, discrete logicsuch as an application specific integrated circuit (ASIC), or otherprogrammed logic device. Logic may also be fully embodied as software.

[0026] “Computer communication”, as used herein, refers to acommunication between two or more computers and/or computer componentsand can be, for example, a network transfer, a file transfer, an applettransfer, an email, a hypertext transfer protocol (HTTP) message, adatagram, an object transfer, a binary large object (BLOB) transfer, andso on. A computer communication can occur across, for example, awireless system (e.g., IEEE 802.11), an Ethernet system (e.g., IEEE802.3), a token ring system (e.g., IEEE 802.5), a local area network(LAN), a wide area network (WAN), a point-to-point system, a circuitswitching system, a packet switching system, and so on.

[0027] An “operable connection” (or a connection by which entities are“operably connected”) is one in which signals and/or actualcommunication flow and/or logical communication flow may be sent and/orreceived. Usually, an operable connection includes a physical interface,an electrical interface, and/or a data interface, but it is to be notedthat an operable connection may consist of differing combinations ofthese or other types of connections sufficient to allow operablecontrol.

[0028] “Data store”, as used herein, refers to a physical and/or logicalentity that can store data. A data store may be, for example, adatabase, a table, a file, a list, a queue, a heap, and so on. A datastore may reside in one logical and/or physical entity and/or may bedistributed between two or more logical and/or physical entities.

[0029] It will be appreciated that some or all of the processes andmethods of the system involve electronic and/or software applicationsthat may be dynamic and flexible processes so that they may be performedin other sequences different than those described herein. It will alsobe appreciated by one of ordinary skill in the art that elementsembodied as software may be implemented using various programmingapproaches such as machine language, procedural, object oriented, and/orartificial intelligence techniques.

[0030] The processing, analyses, and/or other functions described hereinmay also be implemented by functionally equivalent circuits like adigital signal processor circuit, software controlled microprocessor, anapplication specific integrated circuit and the like. Componentsimplemented as software are not limited to any particular programminglanguage. Rather, the description herein provides the information oneskilled in the art may use to fabricate circuits or to generate computersoftware to perform the processing of the system. It will be appreciatedthat some or all of the functions and/or behaviors of the present systemand method may be implemented as logic as defined above.

[0031]FIG. 1 illustrates an example MRI system 100 that may be used withthe example systems and methods described herein and equivalents. Ofcourse, other types of MRI systems can be used and other systemconfigurations are anticipated. The system 100 includes a basic fieldmagnet 1 and a basic field magnet supply 2. The system 100 has gradientcoils 3 for respectively emitting gradient magnetic fields G_(S), G_(P)and G_(R) operated by a gradient coil supply 4. An RF antenna 5 isprovided for generating the RF pulses, and for receiving the resultingmagnetic resonance signals from an object being imaged. While a singleRF antenna 5 is illustrated, it is to be appreciated that there may betwo or more RF antennae that send/receive and/or that are dedicated toeither sending or receiving.

[0032] The RF antenna 5 is operated by an RF transmission/reception unit6. The gradient coil supply 4 and the RF transmission/reception unit 6are operated by a control computer 7 to produce radio frequency pulsesthat are directed to the object to be imaged. The RF antenna 5 receivesor otherwise detects the magnetic resonance signals from the object. Thedetected signals are subject to a transformation process like a FourierTransform (FT) or a fast Fourier Transform (FFT), which generatespixelated image data. The transformation may be performed by an imagecomputer 8 or other similar processing device and/or computer component.The image data may then be shown on a display 9. The object to be imagedtypically is positioned on a table, couch or other type of support thatcan be selectively moved during a scan along an imaging area or bore ofthe MR apparatus.

[0033] An MRI system like system 100 can be employed, for example, inPRF shift thermometry. An example system can accurately monitortemperature changes in the body during interstitial and/or percutaneousinterstitial methods of thermal energy delivery, for example, using avariety of techniques including, but not limited to, laser, RF, andfocused ultrasound. PRF shift thermometry may also be applied inapplications where tissue is heated for gene therapy delivery based onliposomes or the like. The example systems and methods described hereincan, in one example, determine the development of a temperature profilein a given volume of tissue/sample with MR in the presence of temporalvariations in the magnetic field. For example, B₀ of a resistive magnettends to drift as a function of room temperature and it may be desirableto minimize or cancel the effects of the drift.

[0034] While temperature related applications are described herein,other applications including, but not limited to, velocity imaging(e.g., measuring blood flow quantitatively), elastography, measuring ordetermining derivatives of motion (e.g., velocity, acceleration) and soon can benefit from the processing performed by the example systems andmethods described herein. The methods are related to physical parametersthat can be encoded in the phase of an NMR/MRI signal.

[0035] In PRF MR thermometry and other similar applications, temporalinstability of the magnetic field B₀ and misalignment of echoes in theraw data prior to reconstruction contribute to background phasevariations that complicate extracting an accurate temperature profile. Aphase correction scheme referred to as a Variation Correction Algorithm(VCA) combines accurate alignment of echoes in data space (a.k.a.k-space), k-space based phase correction, and extracting wrap free phasedifferences on a pixel-by-pixel basis to mitigate the effects of, forexample, B₀ variations.

[0036] One example PRF application depends on the physics of protons,where they behave differently at different temperatures. For example,temperature changes lead to a shift of the 1H proton resonance frequencyof water by δ=−00.1 ppm/° C. (e.g., the PRF method). Using agradient-recalled echo (GRE) sequence, PRF shift MR thermometry takesadvantage of the tissue-type independence of δ and reconstructs arelative temperature map, ΔT, from a phase difference image, Δφ, via:Δφ(x,y,z,t)=γ·B₀(x,y,z,t)·δ·TEeff·ΔT(x,y,z,t). Here γ=2π·42.58 MHz/T, B₀is the main magnetic field, and TE_(eff) is the effective echo time. Inpractice, temporal instability of B₀ and misalignment of echoescontribute to low-order, time dependent background phase variations (viaFourier transform properties) to the image. This hinders extracting anaccurate temperature profile and hinders future clinical applications ofMR thermometry toward quantitative thermal dose/tissue deathrelationships.

[0037] If B₀ variations did not occur, then performing PRF shiftthermometry might be as simple as gathering a magnetic resonance imagek-space data, performing a Fourier transform reconstruction thatproduces a real image component, R(x,y) and an imaginary imagecomponent, I(x,y), determining a phase angle from the ratio of theimaginary and real components of the image by using, for example,trigonometry, and repeatedly performing the calculations as an item isheated.

[0038] For example, a method to measure temperature change from an MRimage of an object would include acquiring a base line complex (e.g.,real and imaginary) image before heating the object, examining the phaseangle at every position in the image and establishing a reference image,heating the object and determining how much the phase angle changes ateach location in the image by finding the difference betweencorresponding values in the current image (heated image) and thereference image, and calculating how much the temperature has changed.

[0039] However, the systems and methods described herein are not sosimple since the main magnetic field B₀ is not a constant in time orspace, which may cause inaccuracies in the measurements. For example,using the above described simple process for calculating the phasedifference between two images, if the main magnetic field changedbetween the two acquisitions, then the phase angle would changeindependent of temperature simply because of the field B₀ changing.Similarly, if the homogeneity of the magnet changed (e.g., a spatialvariation in the background field), this would give rise to an undesiredphase shifting effect.

[0040] Thus, the example systems and methods described herein includelogic for collecting data in an MR acquisition and using properties ofk-space, Fourier transforms and interpolation schemes to calculate anddetermine a temperature change (or other local phase change) in a MRimage in the presence of temporally and/or spatially dependentvariations in the background magnetic field (e.g. the main magneticfield).

[0041] Thus, turning to FIG. 2, an example correcting system 200 forcorrecting B₀ background phase variations to facilitate accuratelyreconstructing thermal profiles in PRF shift MR thermometry isillustrated. In one example, the correcting system 200 includes an MRIapparatus 210 that generates a plurality of sets of image data. Whilethe MRI apparatus 210 is illustrated and described as being part of thesystem 200, it is to be appreciated that a smaller correcting systemcould receive image data from an MRI apparatus 210 that is external tothe correcting system 200.

[0042] The MRI acquisition apparatus 210 produces an image data that canbe stored, for example, in an image data store 220. The image data mayinclude, for example, a plurality of sets of data, each representing aslice of an object to be imaged. In PRF shift thermometry, each slicewould contain temperature information that can be employed to produce athermal and/or thermal difference image.

[0043] Phase differences may exist between images if the maximumacquired signal in portions of an image (e.g. in a set of data) occursat different locations in the frequency domain, also known as k-space,for different portions (e.g., slices, in different sets of data). Theexample system 200 analyzes k-space data sets and logic shifts the dataso that the peak signals will be aligned with the center of k-space (orsome other constant k-space location) to the nearest fraction of asample. A peak interpolated signal is determined within k-space and thenaccurately aligned to a consistent k-space location throughout theseries of images. Thus, an echo aligner 230 is included to align theplurality of sets of image data with respect to a maximum k-space signallocation in each of the sets of image data. The echo aligner 230 canproduce an aligned data that can then be stored in a data store 240. Theecho aligner 230 also interpolates to the sub sample resolution.

[0044] The system 200 also includes an image phase corrector 250. Theimage phase corrector 250 phase corrects the MRI data employing thephase of a high resolution image of an N×M mask of low-frequency k-space(e.g., Fourier coefficients) coefficients as a phase correction map. Inone example, N could be equal to, greater than or less than M, where Nand M refer to the respective number of rows and columns of datacontained within the mask. The values of N and M could range from zeroto the maximum available row and column dimensions of the image. Inanother example, N is less than five and M is less than five. In anotherexample, N equals M and both are set to three. Depending on theapplication, the image resolution, FOV, and so on, M and N may take on avariety of values. By way of illustration, when the temperature profileis small relative to the FOV, then N and M are typically small. However,this relation can change depending on the size of the object relative tothe FOV, or based on the amount of data acquired. Thus, the image phasecorrector 250 corrects background phase variations in the aligned data240 and forms one or more phase corrected sets of image data that can bestored, for example, in a phase corrected data store 260. Performingk-space based phase correction can include identifying a phase changethat is due to an undesired phase changing event (e.g., B₀ variation)and then manipulating the aligned data to suppress the phase change. Inthe case of an overall B₀ variation, the phase change effect will belocalized near the center of k-space (by Fourier Transform properties)and thus relevant data employed in background suppression can beconcentrated there.

[0045] The system 200 also includes a phase processor 270. The phaseprocessor 270 determines a wrap free phase change from the referenceimage on an element-by-element basis. In one example, the element is apixel that contains phasor data, or the magnitude and phase of thesignal stored in that pixel. In one example, an examination of one ormore relationships between the real and imaginary components of the datacontained within the pixel at two different times leads to the formationof wrap free phase change over the range 0 to 2π. In one example, thedifferent times correspond to an image before (e.g., reference) and animage during heating or some other desired phase shifting event. Thus,the phase processor 270 forms a wrap free phase difference in the phasecorrected sets of image data stored in the phase corrected data store260 and stores the wrap free phase difference data in a wrap free datastore 280.

[0046] In one example, the system 200 includes a display apparatus 290that can employed to display an image derived from the wrap free datastored in the wrap free data store 280 and/or other data stores insystem 200.

[0047] Turning now to FIG. 3, a system 300 that facilitates mitigatingthe effects of undesired phase shifts and quantifying the effects ofdesired phase shifts is illustrated. The system 300 includes a datastore 320 for storing an input signal data received from an MRI system310. In one example, the MRI system 310 is external to the system 300,in another example, the system 300 is integrated into the MRI system310. The input signal data may include, for example, two or more relatedsets of image data taken at two or more different points in time. Forexample, a first set of image data may be acquired before a portion ofan object to be imaged and heated is heated, and one or more second setsof images may be acquired while and/or after the portion of the objectis being heated.

[0048] The system 360 includes a first logic 330 for processing theinput signal data into a processed signal data. The processing attemptsto mitigate the effects of one or more undesired phase shifting eventson the input signal data. By way of illustration, a component of theinput signal may be suppressed in the processed signal data so that itretains a higher signal to noise ratio related to the signal from adesired phase shifting event (e.g., heating).

[0049] The system 300 also includes a second logic 340 for processingthe processed signal data to quantify the effects of one or more desiredphase shifting events. For example, the input signal may have acomponent related to a desired phase shifting event like heating aregion of the object. After the first logic 330 has reduced thecomponent of the input signal attributable to undesired phase shiftingevents (e.g. B₀ variation), then the second logic 340 can focus on thecomponent of the input signal attributable to a desired phase shiftingevent. While the first logic 330 and the second logic 340 areillustrated and described as separate entities, it is to be appreciatedthat both logics may be implemented in a single logic, program, and/orcomputer component, for example.

[0050] Undesired phase shifting events can include, but are not limitedto a variation in a main magnetic field in the MRI system and amisaligning of echoes in the input signal data. The variations in themain magnetic field can include, but are not limited to a temporalvariation, a spatial variation, and a field strength variation. Desiredphase shifting events can include, but are not limited to, heating, amotion change, a velocity change, and an acceleration change.

[0051] In one example, the input image data is a set of k-space datathat contains a peak k-space signal location. Thus, the first logic 330can identify the peak k-space signal location for two or more of therelated sets of image data to facilitate aligning the related sets ofimage data. After identifying one or more k-space signal locations, thefirst logic 330 can then align the two or more related sets of imagedata. In one example, the first logic 330 employs an indirectinterpolation algorithm to identify a peak k-space signal location.

[0052] In one example, the second logic 340 establishes a first set ofprocessed signal data as a reference signal to which subsequent sets ofdata can be compared. Thus, the second logic 340 can compare one or moresecond sets of processed signal data to the reference signal to createone or more sets of difference data. Since the second logic 340 isproducing difference data, the system 300 can include a data store (notillustrated) for storing the reference signal and one or more sets ofdifference data. An output signal data can then be generated by thesecond logic 340 and/or an MRI apparatus and stored in an output signaldata store 350.

[0053] In one example, the system 300 includes a display 360 fordisplaying an image developed from the reference signal and one or moresets of difference data and/or from the output signal data stored in theoutput signal data store 350.

[0054] The second logic 340 can, in one example, determine one or morephase shifts between one or more elements of the processed signal dataaccording to Δφ=φ_(ref)−φ₂. Furthermore, the second logic 340 cancompute φ_(ref) and φ₂ according to φ=tan⁻¹(I(x,y)/R(x,y)).

[0055] The first logic 330 can compute a phase shift due to a variationin B₀ by processing k-space data centered around and local to thelocation of the peak k-space signal. As described herein, the effects ofphase shifts that affect substantially all the object being imaged willbe focused in a small region around one spot in k-space while phaseshifts that affect only a portion of the object being imaged will bedistributed throughout substantially all of k-space. Thus, the secondlogic 340 computes a phase shift due to a temperature change retainingthe k-space effects of local image phase shifts, (where the desiredeffects are distributed remotely from the location of the peak k-spacesignal), and reducing the undesired background shifts, (where thek-space effects of background image phase shifts are concentrated nearthe location of the peak k-space signal). In one example, the secondlogic 340 computes a desired phase shift due to temperature changeaccording to:

Δφ(x,y,t)=γ*B ₀(x,y,t)*δ*TE _(eff) *ΔT(x,y,t),

[0056] where δ=−0.01 ppm/C°, and γ=2π*42.58 MHz/T. Additionally, thesystem 300 can have the first logic 330 extract a wrap free phasedifference on a pixel by pixel basis from the input signal data.

[0057]FIG. 4 illustrates an object 410 to be imaged, where a region ofinterest has been centered in the field of view 400. In the region ofinterest, an area 420 may be subjected to desired phase shifting events(e.g., heated). Phase shifting events that affect substantially all ofthe object 410 will have their effects concentrated near a single point(e.g., the center of k-space) in the k-space associated with the lowfrequency components of the MR data of the object 410. Conversely, phaseshifting events that affect only a small area (e.g., area 420) will havetheir effects distributed substantially throughout all of the k-spaceassociated with the MR data of the object 410.

[0058]FIG. 5 illustrates an example signal 500 that can be received froman MRI apparatus. As part of the data acquisition, the signal 500 issampled by the MR apparatus at various time intervals. In one example,the time intervals are uniform while in another example the timeintervals are not necessarily uniform. The sampled signal 510 isprocessed into an image at a later time. In one example, the maximumamplitude 520 of the sampled signal does not correspond to the truemaximum amplitude 530 of the signal. Therefore, indirect interpolationis performed to facilitate rapidly locating the position of maximuminterpolated amplitude 540, which should correspond to the location ofthe true maximum amplitude 530 to within a pre-determined, configurabletolerance. The indirect interpolation also facilitates shifting the dataset to match the position of maximum interpolated signal amplitude witha pre-determined position in k-space (e.g., center pixel 550). Oneexample indirect interpolation is discussed later, in connection withFIG. 11.

[0059] In view of the exemplary systems shown and described herein,example computer implemented methodologies will be better appreciatedwith reference to the flow diagrams of FIGS. 6 and 7. While for purposesof simplicity of explanation, the illustrated methodologies are shownand described as a series of blocks, it is to be appreciated that themethodologies are not limited by the order of the blocks, as some blockscan occur in different orders and/or concurrently with other blocks fromthat shown and described. Moreover, less than all the illustrated blocksmay be required to implement an example methodology. Furthermore,additional and/or alternative methodologies can employ additional, notillustrated blocks. In one example, methodologies are implemented ascomputer executable instructions and/or operations stored on computerreadable media including, but not limited to an ASIC, a compact disc(CD), a digital versatile disk (DVD), a random access memory (RAM), aread only memory (ROM), a programmable read only memory (PROM), anelectronically erasable programmable read only memory (EEPROM), a disk,a carrier wave, and a memory stick. It is to be appreciated that themethodologies can be implemented in software as that term is definedherein.

[0060] In the flow diagrams, rectangular blocks denote “processingblocks” that may be implemented, for example, in software. Similarly,the diamond shaped blocks denote “decision blocks” or “flow controlblocks” that may also be implemented, for example, in software.Alternatively, and/or additionally, the processing and decision blockscan be implemented in functionally equivalent circuits like a digitalsignal processor (DSP), an ASIC, and the like.

[0061] A flow diagram does not depict syntax for any particularprogramming language, methodology, or style (e.g., procedural,object-oriented). Rather, a flow diagram illustrates functionalinformation one skilled in the art may employ to program software,design circuits, and so on. It is to be appreciated that in someexamples, program elements like temporary variables, routine loops, andso on are not shown.

[0062]FIG. 6 illustrates an example method 600 for processing PRF shiftdata. The method includes, at 610, centering a point of interest of anobject to be imaged in a field of view. The method 600 also includes, at620, receiving MRI data. The MRI data can include a first MRI data froman object to be imaged that is acquired before the object is subjectedto desired phase shifting events (e.g., heating) and potentiallyundesired phase shifting events (e.g., B₀ variation). The MRI data canalso include one or more second MRI data of the object received afterand/or while the object has been subjected to one or more protonresonance frequency altering actions.

[0063] At 630, the method 600 determines a maximum k-space amplitudelocation in the second MRI data. Determining the maximum k-spaceamplitude location can include, in one example, iteratively bisecting agiven search space about an initial guess where linear phase offsets inthe image domain are employed to selectively interpolate midpointsbetween known k-space amplitudes. See, for example, the method describedin connection with FIG. 11.

[0064] At 640, the method includes aligning the data about the newlycomputed maximum k-space amplitude locations. This can includemanipulating the second MRI data to align the maximum k-space amplitudelocations with an assumed k-space center to within a tolerance. In oneexample the tolerance is about {fraction (1/128)}th of a cycle/FOV.

[0065] At 650, the method 600 phase corrects the MRI data. In oneexample, phase correcting the MRI data comprises employing the phase ofa high resolution image of an N×M mask of low-frequency Fouriercoefficients as a phase correction map. In one example, N is less thanfive and M is less than five. In another example, N equals M and bothare set to three. In one example, N could be equal to, greater than, orless than M, where N and M refer to the respective rows and columns ofdata contained within the mask. The values of N and M could range fromzero to the maximum respective row and column dimensions of the image.

[0066] The method 600 also includes, at 660, determining a wrap freephase change from the reference image on an element-by-element basis. Inone example, the element is a pixel that contains phasor data, or themagnitude and phase of the signal stored in that pixel. In one example,an examination of one or more relationships between the real andimaginary components of the image domain data contained within the pixelat two different times leads to the formation of wrap free phase changeover the range 0 to 2π. In one example, the different times correspondto an image before (e.g., reference) and an image during heating or someother desired phase shifting event.

[0067] Turning now to FIG. 7, an example method 700 for calculatingtemperature change in an MR image in the presence of variations liketemporally and/or spatially dependent variations in B₀ is illustrated.The method includes, at 710, receiving an MRI signal. Once the MRIsignal has been received, the method 700 accurately aligns echoes in asignal. In one example, accurately aligning echoes in a signal includesdigitizing the signal at 720, sampling the signal at uniform timeintervals to produce a sampled data at 730 and indirectly interpolatingthe sampled data to find a peak signal location at 740. It is to beappreciated that 720 through 740 can be performed serially and/orsubstantially in parallel, and that 720 through 740 can be performedafter a sufficient amount of MRI signal has been received at 710.

[0068] The method 700 also includes, at 750, applying a phase shift toone or more sets of data encoded in the signal to align the one or moresets of data. Once the data has been aligned, then the method 700 canperform k-space based phase correction. Performing k-space based phasecorrection can include, at 760, identifying a phase change that is dueto an undesired phase changing event (e.g., B₀ variation) and thenmanipulating the data to suppress the phase change. In the case of anoverall B₀ variation, the phase change effect will be localized near thecenter of k-space and thus suppression efforts can be focused there.

[0069] At 770, the method 700 includes correcting for the undesiredphase shift. This may include, for example, manipulating the data tosuppress the undesired phase change. In one example, manipulating thedata to suppress the undesired phase change includes creating anopposite effect of what is seen at the center of k-space. Additionally,the method 700 may include extracting wrap free phase differences on aunit by unit basis. In one example, the unit is a pixel.

[0070] At 780, the method identifies a desired shift. For example, inPRF shift thermometry, the desired shift is caused by temperature changeof a local region of interest in a field of view. Thus, since the changeis local, the shift due to the local change is likely to be distributedthroughout the k-space. Therefore, the previous manipulation of thecenter of k-space is likely to leave the distributed effects of localphase change substantially intact.

[0071] Thus, one example method includes receiving a signal generated ina magnetic coil, digitizing the signal and sampling the signal atuniform time intervals. However, the sampling might not sample thelocation of the peak so an interpolation is performed, (e.g., anindirect interpolation), to determine the peak signal location. When thepeak location is found, a frequency shift is applied to the k-space datato align the peak signal location in each of the images to a consistentlocation. The indirect interpolation technique facilitates selectivelyfinding points in between the coarsely sampled points so that thealgorithm rapidly converges to the peak. In one example, it uses abinary search scheme to chose which points will be interpolated.

[0072] Once the image data is aligned in k-space, one example methoddetermines how to suppress one of B₀ or ΔT but not the other, leaving animage that can display the effects of the desired phase shifting event.In one example, an assumption is made that the extent of ΔT (thetemperature change to determine) is relatively small relative to thefield of view, and the change in B₀ (magnetic field) is over the entirefield of view with gradual variations.

[0073] From a Fourier transform perspective, things that are small inspace have their data substantially everywhere in data collectionk-space. For example, little areas in the image correspond to largeregions of k-space, and things that occur in large scales in the imagecorrespond to very small regions in k-space.

[0074] With this in mind, local changes in the image (e.g., temperaturechange in a spot) will have an effect substantially everywhere ink-space. However, B₀ is changing substantially everywhere over theimage, but its effect is focused about one spot in k-space (e.g. thecenter of k-space). Thus, if the method corrects mostly for the effectof the main magnetic field, it will only slightly affect the fidelity ofthe temperature data in the image. Although temperature data does existat the center of k-space, the amount of temperature data affected isminimal when correcting for B₀.

[0075]FIG. 8 illustrates another example system 800 for correcting forthe effects of undesired phase shifting events. The system 800 includesan MRI apparatus 810 into which have been integrated a first logic 820,a second logic 830, and a memory 840. Those skilled in the art willrecognize that an MRI apparatus 810 may already have one or more logicsthat can be reprogrammed and/or replaced (e.g., ROMs installed).Similarly, those skilled in the art will recognize that an MRI apparatus810 may already have a memory 840. Thus, the system 800 can, in oneexample, be implemented in existing MRI apparatus by reprogramming,reconfiguring, and/or installing new parts.

[0076] In one test that exercised example systems like those describedherein, a phantom of Natrosol (Aqualon Co., Hopewell, Va., USA) wasconstructed to mimic a block of tissue. The phantom was allowed toequilibrate for 3 hours to the imaging room temperature. Raw k-spacedata was collected every minute for one hour on a Siemens 0.2T openimager using an echo shifted GRE sequence (TR 19.4 ms, TE_(eff)28.9 ms,α=30°, FOV=300 mm², Matrix=128², NA=2, BW=78 Hz/pixel) (TR=relaxationtime, TE=echo time). Center frequency and shim currents remainedunaltered during the acquisition of 60 data sets in total. One skilledin the art will appreciate that this was but one test, and thatvariations in one or more settings are contemplated.

[0077] The correction process, referred to in one example as theVariation Correction Algorithm (VCA), was executed in four stages.First, the point of interest in the object was centered in the field ofview (FOV). Second, the maximum k-space amplitude position was alignedwith an assumed k-space center to the nearest {fraction (1/128)}th of acycle/FOV. This was accomplished with iterative bisection of a givensearch space about an initial guess, where linear phase offsets in theimage domain were used to selectively interpolate midpoints betweenknown k-space amplitudes. Third, the phase of a high-resolution image ofan N×N mask of low-frequency Fourier coefficients served as the phasecorrection map. Then the wrap free phase change from the reference imagewas determined on a pixel-by-pixel basis by examining the relationshipsbetween the two phasors over the range [0, 2π]. Those skilled in the artwill appreciate that the stages could be performed in other orders andthat a greater number of stages could be employed.

[0078] In the example, to analyze suppression, the images were processedwith a mask size of N×N (N=0, 1 . . . 8, 16, 32, 64, 128). Using image 1as the reference, 59 phase difference images were formed. In a 35×35region of interest (ROI) centered in the FOV, the mean and standarddeviation (SD) of residual phase difference were computed. Temporalbehavior of suppression errors was summarized by the mean±SD, andmaximum(max) and minimum(min) of all 59 estimators for each measure andchoice of N. A different number of images and estimators could also beused.

[0079] In the example, to analyze profile fidelity, a simulatedGaussian-shaped profile (ΔTmax=53° C., radius=15 pixels, σ=4.38 pixels)was applied in the center of the object prior to the example VCAprocessing described above. The applied thermal profile was subtractedfrom the temperature difference to yield a profile error map. For eachimage, the mean, SD, max and min error along the ΔT=23° C. contour (60°C. line) was determined for each N. The temporal behavior (mean, SD,max, min) of each profile fidelity estimator was extracted. In addition,profile distortions were noted.

[0080] Useful suppression performance was achieved at several values forN, with one example being N=3. At N=3, the typical mean residual errorwas: (mean, SD, max, min)=(−0.1° C., 0.3° C., 0.6° C., −1.2° C.); andthe typical SD of residual error was: (mean, SD, max, min)=(3.5° C.,0.5° C., 4.5° C., 2.1° C.).

[0081]FIG. 9 illustrates a computer 900 that includes a processor 902, amemory 904, a disk 906, input/output ports 910, and a network interface912 operably connected by a bus 908. Executable components of thesystems described herein may be located on a computer like computer 900.Similarly, computer executable methods described herein may be performedon a computer like computer 900. It is to be appreciated that othercomputers and/or computer components may also be employed with thesystems and methods described herein. Furthermore, it is to beappreciated that the computer 900 can be located locally to an MRIsystem, remotely to an MRI system, and/or can be embedded in an MRIsystem.

[0082] The processor 902 can be a variety of various processorsincluding dual microprocessor and other multi-processor architectures.The memory 904 can include volatile memory and/or non-volatile memory.The non-volatile memory can include, but is not limited to, read onlymemory (ROM), programmable read only memory (PROM), electricallyprogrammable read only memory (EPROM), electrically erasableprogrammable read only memory (EEPROM), and the like. Volatile memorycan include, for example, random access memory (RAM), synchronous RAM(SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rateSDRAM (DDR SDRAM), and direct RAM bus RAM (DRRAM). The disk 906 caninclude, but is not limited to, devices like a magnetic disk drive, afloppy disk drive, a tape drive, a Zip drive, a flash memory card,and/or a memory stick. Furthermore, the disk 906 can include opticaldrives like, a compact disk ROM (CD-ROM), a CD recordable drive (CD-Rdrive), a CD rewriteable drive (CD-RW drive) and/or a digital versatileROM drive (DVD ROM). The memory 904 can store processes 914 and/or data916, for example. The disk 906 and/or memory 904 can store an operatingsystem that controls and allocates resources of the computer 900.

[0083] The bus 908 can be a single internal bus interconnectarchitecture and/or other bus architectures. The bus 908 can be of avariety of types including, but not limited to, a memory bus or memorycontroller, a peripheral bus or external bus, and/or a local bus. Thelocal bus can be of varieties including, but not limited to, anindustrial standard architecture (ISA) bus, a microchannel architecture(MSA) bus, an extended ISA (EISA) bus, a peripheral componentinterconnect (PCI) bus, a universal serial bus (USB), and a smallcomputer systems interface (SCSI) bus.

[0084] The computer 900 interacts with input/output devices 918 viainput/output ports 910. Input/output devices 918 can include, but arenot limited to, a keyboard, a microphone, a pointing and selectiondevice, cameras, video cards, displays, and the like. The input/outputports 910 can include but are not limited to, serial ports, parallelports, and USB ports.

[0085] The computer 900 can operate in a network environment and thus isconnected to a network 920 by a network interface 912. Through thenetwork 920, the computer 900 may be logically connected to a remotecomputer 922. The network 920 includes, but is not limited to, localarea networks (LAN), wide area networks (WAN), and other networks. Thenetwork interface 912 can connect to local area network technologiesincluding, but not limited to, fiber distributed data interface (FDDI),copper distributed data interface (CDDI), ethernet/IEEE 802.3, tokenring/IEEE 802.5, and the like. Similarly, the network interface 912 canconnect to wide area network technologies including, but not limited to,point to point links, and circuit switching networks like integratedservices digital networks (ISDN), packet switching networks, and digitalsubscriber lines (DSL).

[0086] Referring now to FIG. 10, an application programming interface(API) 1000 is illustrated providing access to a system 1010 thatincludes a phase shift processor. The phase shift processor 1010facilitates distinguishing and manipulating PRF shift related data. TheAPI 1000 can be employed, for example, by programmers 1020 and/orprocesses 1030 to gain access to processing performed by the system1010. For example, a programmer 1020 can write a program to access(e.g., to invoke its operation, to monitor its operation, to access itsfunctionality) the system 1010 where writing the program is facilitatedby the presence of the API 1000. Rather than the programmer 1020 havingto understand the internals of the system 1010, the programmer's task issimplified by merely having to learn the interface 1000 to the system1010. This facilitates encapsulating the functionality of the system1010 while exposing that functionality. Similarly, the API 1000 can beemployed to provide data values to the system 1010 and/or to retrievedata values from the system 1010.

[0087] For example, a programmer 1020 may wish to present image data tothe system 1010 and thus the programmer 1020 may employ an image datainterface 1040 component of the API 1000. Similarly, the programmer 1020may wish to present an alignment data to the system 1010 and thus mayemploy an alignment data interface 1050 component of the API 1000. Afterreceiving the image data and alignment data, the system 1010 may, forexample, pass a phase difference data to a process 1030 via a phasedifference data interface 1060 component of the API 1000.

[0088] Thus, in one example of the API 1000, a set of applicationprogram interfaces can be stored on a computer-readable medium. Theinterfaces can be executed by a computer component to gain access to asystem for processing PRF phase shift data. Interfaces can include, butare not limited to, a first interface that facilitates communicating animage data associated with one or more MRI signals, a second interfacethat facilitates communicating an alignment data, and a third interfacethat facilitates communicating a phase difference data generated fromthe image data and the alignment data.

[0089] Concerning indirect interpolation, the following discussionconcerning FIG. 11 facilitates understanding one example method. To findthe maximum amplitude position in two dimensional K-space to sub-sampleresolution for accurate echo alignment, some form of interpolation is beperformed to increase the numerical resolution of K-space. By Fouriertransform properties that are known in the art, the two dimensional FastFourier Transform of a zero-padded matrix of object pixel data willyield a sinc interpolated K-space with increased numerical resolution.Given finite memory and computational resources and desired numericalresolution, the factor to which a matrix of data can be zero-padded islimited.

[0090] The shift properties of the Fourier transform, namely linearphase variations applied in one domain (e.g., in the image) causeposition shifts in the other domain (e.g., K-space), and thus can beused to selectively query the data that lies between coarsely spacedsamples in K-space. It is apparent to those in the art that the phaseshift could be applied in K-space to interpolate between pixels of theimage. In essence, small (e.g., {fraction (1/128)}^(th) of a cycle of 2πacross the field of view) increments of the global linear phase terms ofthe image will slightly shift K-space so that unknown data at knownposition offsets from a coarsely spaced sample will appear to have beensampled. Since the applied position shift is known, the value of thepreviously unknown data can be determined and then remapped to itsoriginal offset from the coarsely sampled signal.

[0091] Selectively interpolating between adjacent pixels in one Fourierdomain by applying linear phase variations in the complementary Fourierdomain is called Indirect Interpolation.

[0092] However, all possible combinations of phase shifts need not betested to determine the maximum interpolated amplitude position. IfK-space is assumed to monotonically decrease in the neighborhood of themaximum amplitude, binary search methods can control the IndirectInterpolation between the elements of a coarsely sampled signal. Thefollowing two conditions are assumed to be true for any arbitrary dataset:

[0093] The maximum amplitude lies within a finite neighborhood aboutsome initial guess in K-space; and,

[0094] The location of maximum amplitude should be found to the nearest(½)^(Af) of a sample.

[0095]FIG. 11 summarizes the three steps of the alignment process thatare repeated until the desired sub-sample resolution is obtained for aneighborhood of ±1 sample and alignment tolerance of {fraction(1/128)}^(th) of a sample (e.g., Af=7).

[0096]1110 illustrates initializing a matrix of known amplitudes. Theamplitude of the initial guess is placed in the center of a 5×5 matrixof zeros; typically, the initial estimate is the peak signal in the rawdata matrix. The amplitudes that fall ±1 sample along the Kx and Kydirections from that guess are placed along the edge of the matrix atcorresponding positions. This defines the extent of the initial searchspace for the first iteration (iter=1) of interpolation.

[0097]1120 illustrates querying and recording unknown amplitudes. Themidpoint between any two adjacent known amplitudes in 1110 correspondsto an unknown datum that exists (½)^(iter) row and/or column away.Indirect Interpolation is applied sixteen times (e.g., 9 of 25amplitudes are already known) to “bisect” any interval between knownamplitudes.

[0098]1130 illustrates preparing the matrices for another round ofinterpolation. Since monotonicity was assumed, a search space thatextends±(½)^(iter) row and/or column from the current maximum amplitudeis the smallest space that still contains the unique maximum amplitude.Hence, the current maximum amplitude (shaded with vertical bars in 1120)is placed in the center of a 5×5 matrix of zeros, and the values thatare ±1 element (e.g., ±½ sample from the position of the current maximumfor iter=1) away in the recently filled amplitude matrix are arranged intheir corresponding positions along the edges as per 1130. If thecurrent maximum amplitude falls on an edge of the filled matrix, anadditional set of Indirect Interpolations is performed to capture thenecessary data that is not in the current amplitude matrix. All othersearch regions, as demonstrated by the shaded overlay in 1130, arediscarded as they cannot contain the global maximum amplitude and neednot be tested further.

[0099] Iterations of the “Query” and “Prepare” stages more finelyresamples the search space about the current maximum amplitude by afactor of two in both the Kx and Ky directions until the algorithmconverges to the unique maximum interpolated amplitude contained withinthe search space. In other words, uncertainty in the position of maximumamplitude decreases from ±1, to ±½, to ±¼ to . . . ±(½) ^(Af) of asample with each iteration. Similarly, iterations of the searchalgorithm can be said to reduce the search space by a factor of four. Atconvergence, the corresponding Kx and Ky frequency offset is the scaledamount of linear phase in the image domain that will shift the peakamplitude to the location of the original peak signal estimate. Thus,two sets of scaled amounts of linear phase are applied simultaneously toalign the data. One set shifts the maximum interpolated amplitude to thelocation of the original peak estimate, and the other set corresponds tointeger cycles of linear phase that will shift the data from theoriginal peak location to the center of K-space.

[0100] Those skilled in the art will appreciate that FIG. 11 illustratesone example, and that other methods can be employed. This echo alignmentprocess (Indirect Interpolation under the control of binary searchprinciples) can be extended to cases where: the size of the initialsearch space could be larger than ±1 sample; the initial guess could bea signal feature other than the position of the maximum amplitude in thecoarsely sampled signal; the location of some other signalcharacteristic maximum (e.g., phase, power) is the search objective; thesearch tolerance is greater or less than {fraction (1/128)}^(th) of asample; data other than signal amplitude is queried and recorded; datais retained through all iterations; data is represented by more than twodimensions; and so on.

[0101] The systems, methods, and objects described herein may be stored,for example, on a computer readable media. Media can include, but arenot limited to, an ASIC, a CD, a DVD, a RAM, a ROM, a PROM, a disk, acarrier wave, a memory stick, and the like. Thus, an example computerreadable medium can store computer executable instructions for computerimplemented methods described and claimed herein. Similarly, a computerreadable medium can store computer executable components of systemsdescribed and claimed herein.

[0102] What has been described above includes several examples. It is,of course, not possible to describe every conceivable combination ofcomponents or methodologies for purposes of describing the methods,systems, computer readable media and so on employed in PRF shiftthermometry in an MRI system. However, one of ordinary skill in the artmay recognize that further combinations and permutations are possible.Accordingly, this application is intended to embrace alterations,modifications, and variations that fall within the scope of the appendedclaims. Furthermore, to the extent that the term “includes” is employedin the detailed description or the claims, it is intended to beinclusive in a manner similar to the term “comprising” as that term isinterpreted when employed as a transitional word in a claim. Furtherstill, to the extent that the term “or” is employed in the claims (e.g.,A or B) it is intended to mean “A or B or both”. When the author intendsto indicate “only A or B but not both”, then the author will employ theterm “A or B but not both”. Thus, use of the term “or” herein is theinclusive, and not the exclusive, use. See BRYAN A. GARNER, A DICTIONARYOF MODERN LEGAL USAGE 624 (2d Ed. 1995).

What is claimed is:
 1. A system, comprising: a data store for storing aninput signal data received from an MRI or NMR system, where the inputsignal data includes two or more related sets of image data; a firstlogic for processing the input signal data into a processed signal data,where the effects of one or more undesired phase shifting events on theinput signal data are reduced in the processed signal data; and a secondlogic for processing the processed signal data to quantify the effectsof one or more desired phase shifting events.
 2. The system of claim 1,where the undesired phase shifting events are one or more of a variationin a main magnetic field in the MRI or NMR system and a misaligning ofechoes in the input signal data.
 3. The system of claim 2, where thevariation in the main magnetic field is one or more of a temporalvariation, a spatial variation, and a field strength variation.
 4. Thesystem of claim 1, where a desired phase shifting event is a temperaturechange.
 5. The system of claim 1, where the desired phase shifting eventis one or more of a motion change, a velocity change, and anacceleration change.
 6. The system of claim 1, where a set of image datais a set of k-space data that contains a peak k-space signal location.7. The system of claim 6, where the first logic identifies the peakk-space signal location for two or more of the related sets of imagedata and aligns the two or more related sets of image data.
 8. Thesystem of claim 7, where the first logic employs an indirectinterpolation algorithm to identify the peak k-space signal location. 9.The system of claim 1, where the second logic establishes a first set ofprocessed signal data as a reference signal.
 10. The system of claim 9,where the second logic compares one or more second sets of processedsignal data to the reference signal to create one or more sets ofdifference data.
 11. The system of claim 10, comprising a data store forstoring the reference signal and one or more sets of difference data.12. The system of claim 10, comprising a display for displaying an imagedeveloped from the reference signal and one or more sets of differencedata.
 13. The system of claim 10, where the second logic creates the oneor more sets of difference data by determining one or more phase shiftsbetween one or more elements of the processed signal data according toΔφ=φ_(ref)−φ₂.
 14. The system of claim 13, where the second logiccomputes φ_(ref) and φ₂ according to φ=tan⁻¹(I(x,y)/R(x,y) ).
 15. Thesystem of claim 1, where the first logic processes the input signal bycomputing a phase shift due to a variation in B₀ by processing k-spacedata centered around and local to a peak k-space signal location. 16.The system of claim 15, where the second logic quantifying the effectsof one or more desired phase shifting events includes computing a phaseshift due to a temperature change by processing k-space data in theabsence of one or more contributions of data near and local to the peakk-space signal location.
 17. The system of claim 1, where the firstlogic extracts a wrap free phase difference on a pixel by pixel basisfrom the input signal data.
 18. The system of claim 10, comprising thesecond logic quantifying the effects of temperature change as a desiredphase shifting event according to: Δφ(x,y,t)=γ*B ₀(x,y,t)*δ*TE _(eff)*ΔT(x,y,t).
 19. The system of claim 18, where δ is approximately −0.01ppm/C.°.
 20. The system of claim 18, where γ is approximately 2π*43MHz/T.
 21. The system of claim 1, where the first logic and the secondlogic are implemented in a single logic.
 22. A computer readable mediumstoring computer executable components of the system of claim
 1. 23. Asystem for correcting B₀ background phase variations to facilitateaccurately reconstructing thermal profiles in proton resonance frequency(PRF) shift MR thermometry, comprising: an MRI apparatus for generatinga plurality of sets of image data; an echo aligner for aligning theplurality of sets of image data with respect to a maximum k-space signallocation in each of the sets of image data; an image phase corrector forcorrecting background phase variations in the aligned sets of image datato form one or more phase corrected sets of image data; a phaseprocessor for forming a wrap free phase difference in the phasecorrected sets of image data to form one or more wrap free phasedifference data; and an imaging apparatus for reconstructing a thermalprofile from the one or more wrap free difference data.
 24. The systemof claim 23, where the background phase variations are induced by one ormore of B₀ field inhomogeneity, temporal instability, and spatialinstability.
 25. A computer readable medium storing computer executablecomponents of the system of claim
 23. 26. A method, comprising:centering a point of interest of an object to be imaged in a field ofview; receiving a first MRI data of the object; establishing a referenceimage from the first MRI data; receiving one or more second MRI data ofthe object, where the object has been subjected to one or more protonresonance frequency altering actions; determining a maximum k-spaceamplitude location in one or more of the second MRI data; manipulatingthe first MRI data to align the maximum k-space amplitude locations withan assumed k-space center to within a tolerance; phase correcting thefirst MRI data; determining a wrap free phase change from the referenceimage on an element-by-element basis; and producing a display data thatcan be displayed, where the display data facilitates displaying theresults of a desired phase shift event on the object to be imaged. 27.The method of claim 26, where determining a maximum k-space amplitudelocation comprises iteratively bisecting a given search space about aninitial guess where one or more linear phase offsets in an image domainare employed to selectively interpolate midpoints between one or moreknown k-space amplitudes.
 28. The method of claim 26, where phasecorrecting the first MRI data comprises employing the phase of a lowresolution image of an N×M mask of low-frequency Fourier co-efficientsas a phase correction map, where N and M refer to the respective numberof rows and columns of data contained within the mask, M and N beingintegers.
 29. The method of claim 28, where N is less than 5 and where Mis less than
 5. 30. The method of claim 28, where N and M are related toone or more of the FOV and the size of the expected site of phasevariation.
 31. The method of claim 26, where the tolerance is about{fraction (1/128)}th of a cycle/FOV.
 32. The method of claim 26, wheredetermining the wrap free phase change comprises examining one or morerelationships between two phasors over the range 0 to 2π.
 33. The methodof claim 26, where the element is a pixel.
 34. A method for calculatinga temperature change in an MR image in the presence of temporally and/orspatially dependent variations in B₀, comprising: receiving an MRIsignal; accurately aligning echoes in a signal; phase correcting thesignal; extracting wrap free phase differences from the signal on a unitby unit basis; and calculating the temperature change from the signal.35. The method of claim 34, where accurately aligning echoes in a signalcomprises: digitizing the signal; sampling the signal at time intervalsto produce a sampled data; indirectly interpolating the sampled data tofind a peak signal location; and applying a phase shift to one or moresets of data encoded in the signal to align the one or more sets ofdata.
 36. The method of claim 35, where the time intervals are uniform.37. The method of claim 34, where phase correcting the signal comprises:identifying the phase change due to an undesired phase changing event,where the phase change effect will be localized near the center ofk-space; and manipulating the data to suppress the undesired phasechange.
 38. The method of claim 37, where manipulating the data tosuppress the undesired phase change comprises creating an oppositeeffect of what is seen at the center of k-space.
 39. A computer readablemedium storing computer executable instructions operable to performcomputer executable portions of the method of claim
 35. 40. A set ofapplication programming interfaces embodied on a computer readablemedium for execution by a computer component in conjunction with anapplication program that processes PRF phase shift data, comprising: afirst interface that facilitates communicating image data; a secondinterface that facilitates communicating alignment data; and a thirdinterface that facilitates communicating phase difference data where thephase difference data is produced from the image data and the alignmentdata.
 41. A system for processing PRF shift MRI data, comprising: meansfor acquiring a PRF shift MRI data; means for distinguishing a desiredPRF shift from an undesired PRF shift in the PRF shift MRI data; andmeans for manipulating the PRF shift MRI data to reduce the effects ofthe undesired PRF shift.
 42. A system for processing PRF shift MRI data,comprising: a PRF shift data corrector; and a magnetic resonance imager.43. The system of claim 42, where the magnetic resonance imagercomprises: a polarizing magnetic field generator for generating apolarizing magnetic field in an examination region; an RF generator forgenerating an excitation magnetic field that produces transversemagnetization in nuclei subjected to the polarizing magnetic field; asensor for sensing a magnetic resonance signal produced by thetransverse magnetization; a gradient generator for generating a magneticfield gradient to impart a read component into the magnetic resonancesignal, where the read component indicates a location of a transverselymagnetized nuclei along a first projection axis, the gradient generatorgenerating subsequent magnetic field gradients to impart subsequent readcomponents into the magnetic resonance signal that indicates subsequentlocations of the transversely magnetized nuclei along one or moresubsequent projection axes; a pulse controller operably coupled to theRF generator, the gradient generator, and the sensor, the pulsecontroller conducting a scan in which a series of data points areacquired at read points along a radial axis to form a magnetic resonancedata view, one or more subsequent magnetic resonance data views defininga magnetic resonance data set; a data store for storing the magneticresonance data set; and a processor for reconstructing an image for adisplay from the stored magnetic resonance data set.
 44. The system ofclaim 43, the PRF shift data corrector comprising: a first logic forprocessing the magnetic resonance data set to mitigate the effects of anundesired phase shifting event in the magnetic resonance data set; and asecond logic for quantifying the effects of a desired phase shiftingevent after the processing performed by the first logic.
 45. The systemof claim 44, where the first logic and the second logic are embodied ina single logic.